论文标题
使用价值提案的贝叶斯优化可混合变量输入
Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals
论文作者
论文摘要
在分类变量和连续变量上都定义了许多实际优化问题，但是有效的优化方法（例如Asbayesian优化）并未设计出这种可混合变量的搜索空间的设计。该问题的最新方法将分类变量的选择作为匪徒问题，并与优化连续变量的BO组件独立运行。在本文中，我们采用了整体观点，旨在巩固单个采集指标下的分类和连续子空间的优化。我们从预期的IMPROVEMENT标准中得出候选者，我们将其称为价值建议，并使用这些建议对输入的分类和连续组件进行选择。我们表明，这种统一方法在几种混合变量的黑盒优化任务上大大优于现有的混合变量优化方法。
Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not designed tohandle such mixed-variable search spaces. Recent approaches to this problem cast the selection of the categorical variables as a bandit problem, operating independently alongside a BO component which optimises the continuous variables. In this paper, we adopt a holistic view and aim to consolidate optimisation of the categorical and continuous sub-spaces under a single acquisition metric. We derive candidates from the ExpectedImprovement criterion, which we call value proposals, and use these proposals to make selections on both the categorical and continuous components of the input. We show that this unified approach significantly outperforms existing mixed-variable optimisation approaches across several mixed-variable black-box optimisation tasks.
